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csabakecskemeti 
posted an update 3 days ago
Post
2653
Testing Training on AMD/ROCm the first time!

I've got my hands on an AMD Instinct MI100. It's about the same price used as a V100 but on paper has more TOPS (V100 14TOPS vs MI100 23TOPS) also the HBM has faster clock so the memory bandwidth is 1.2TB/s.
For quantized inference it's a beast (MI50 was also surprisingly fast)

For LORA training with this quick test I could not make the bnb config works so I'm running the FT on the fill size model.

Will share all the install, setup and setting I've learned in a blog post, together with the cooling shroud 3D design.

FWIW, the MI100 was released after the A100, 3 years after the V100... that says something :) Also it's the matrix / tensor core mixed or reduced precision FLOPs that are of interest not the float32 FLOPS which are the 14 & 23 numbers..

·

All correct!
What I've called out: a used MI100 is in the same price range as used V100 PCIe that's why I'm comparing with that.

And yes you're right FP16 performance would be more useful to mention (AFAIK V100 112TFLOPS, MI100 184TFLOPS) but regarding comparison it shows the same 164% (claimed) performance for MI100.

Please NOTE I'm building my hobby AI infra at home for myself, so mainly constrained by TOPS/$ :D

I also use MI100's, and I also had a very frustrating time trying to make bnb work, even with their new branch. It is frustrating that I am able to make fine-tunings on larger models on my Mac than I am with 8x MI100.

So far I'm managed to have a working bnb up:

(bnbtest) kecso@gpu-testbench2:~/bitsandbytes/examples$ python -m bitsandbytes
g++ (Ubuntu 14.2.0-4ubuntu2) 14.2.0
Copyright (C) 2024 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++ BUG REPORT INFORMATION ++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++ OTHER +++++++++++++++++++++++++++
ROCm specs: rocm_version_string='63', rocm_version_tuple=(6, 3)
PyTorch settings found: ROCM_VERSION=63
The directory listed in your path is found to be non-existent: local/gpu-testbench2
The directory listed in your path is found to be non-existent: @/tmp/.ICE-unix/2803,unix/gpu-testbench2
The directory listed in your path is found to be non-existent: /etc/xdg/xdg-ubuntu
The directory listed in your path is found to be non-existent: /org/gnome/Terminal/screen/6bd83ab2_fd9f_4990_876a_527ef8117ef6
The directory listed in your path is found to be non-existent: //debuginfod.ubuntu.com
WARNING! ROCm runtime files not found in any environmental path.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++ DEBUG INFO END ++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Checking that the library is importable and ROCm is callable...
SUCCESS!
Installation was successful!

It's able to load the model to vram, but inference fails:
Exception: cublasLt ran into an error!

This is the main problem with anything not NVIDIA. The software is painful!
Keep trying...